Atmospheric composition studies on weather and climate timescales require
flexible, scalable models. The ICOsahedral Nonhydrostatic model with Aerosols
and Reactive Trace gases (ICON-ART) provides such an environment. Here, we
introduce the most up-to-date version of the flexible tracer framework for
ICON-ART and explain its application in one numerical weather forecast and
one climate related case study. We demonstrate the implementation of
idealised tracers and chemistry tendencies of different complexity using the
ART infrastructure. Using different ICON physics configurations for weather
and climate with ART, we perform integrations on different timescales,
illustrating the model's performance. First, we present a hindcast
experiment for the 2002 ozone hole split with two different ozone chemistry
schemes using the numerical weather prediction physics configuration. We
compare the hindcast with observations and discuss the confinement of the
vortex split using an idealised tracer diagnostic. Secondly, we study
AMIP-type integrations using a simplified chemistry scheme in conjunction with the
climate physics configuration. We use two different simulations: the
interactive simulation, where modelled ozone is coupled back to the radiation
scheme, and the non-interactive simulation that uses a default background
climatology of ozone. Additionally, we introduce changes of water vapour by
methane oxidation for the interactive simulation. We discuss the impact of
stratospheric ozone and water vapour variations in the interactive and
non-interactive integrations on the water vapour tape recorder, as a measure
of tropical upwelling changes. Additionally we explain the seasonal evolution
and latitudinal distribution of the age of air. The age of air is a measure
of the strength of the meridional overturning circulation with young air in
the tropical upwelling region and older air in polar winter downwelling
regions. We conclude that our flexible tracer framework allows for
tailor-made configurations of ICON-ART in weather and climate applications
that are easy to configure and run well.

Until recently, most global numerical weather prediction (NWP) and climate
models were built upon the approximate hydrostatic primitive set of
equations, which hindered their application on convective scales (O(1 km))
or below. With the advent of next-generation global modelling systems like
MPAS (Skamarock et al., 2012), NICAM (Tomita and Satoh, 2004), UM-UKCA
(Hewitt et al., 2011) or ICON (Zängl et al., 2015), this restriction has been
overcome, as they rely on a fully compressible set of hydro-thermodynamical
equations (i.e. the acceleration term dw∕dt in the vertical
momentum equation is no longer neglected). In principle, this allows the same
dynamical core to be applied across the entire range of temporal and spatial
scales that exist in the atmosphere. Thus, simulations ranging from a few
hours to hundreds of years may become more consistent and advances of one
scale or application can more easily be transferred to another. However, the
construction of scale-aware physical parameterisations that can be
applied over the entire range of scales remains a challenge
(Chun et al., 2016; Rauscher et al., 2013).

The extended modelling system ICON-ART (Rieger et al., 2015) is a representative
of a next-generation system. ICON (Zängl et al., 2015) is a joint development of
Deutscher Wetterdienst (DWD) and Max Planck Institute for Meteorology
(MPI-M). The dynamical core of ICON is based on the nonhydrostatic
formulation of the vertical momentum equation. Thus, ICON allows simulations
with high horizontal resolutions, up to grid spacings of a few hundreds of
metres. However, while the dynamical core of ICON is unified, individual
applications like large eddy simulations (LESs), NWP or climate projections,
e.g. EC(MWF) HAM(burg) (ECHAM),
currently use different parameterisations of physical processes
(Dipankar et al., 2015; Heinze et al., 2017).

ICON offers the possibility of local grid refinements, also called
“nesting”. The nesting provides the option of two-way interactions between
the (global) coarse domain and the higher-resolution local domain.
Hierarchical nesting and several domains at the same nesting level are
possible. ICON already provides benefits in various application fields, e.g.
in the High Definition Clouds and Precipitation for advancing Climate
Prediction (HD(CP)2) project. Selected results of the HD(CP)2
project are described in Heinze et al. (2017). Within this project, the
configuration of the LES physical configuration has been developed and is
used to achieve a better understanding of, for example, processes that are
related to precipitation. Results will help to improve future climate
projections on coarser resolutions.

Since January 2015, ICON has been running operationally at DWD for weather forecasts on
the global scale with a grid spacing of 13 km. Since June 2015, a
local grid refinement over Europe with a horizontal resolution of
6.5 km has been in operational use as well.

ART is an additional module for ICON, developed at the Karlsruhe Institute of
Technology. It contributes to the goal of a unified global next-generation
modelling system with a variety of applications in the field of atmospheric
composition sciences. A variant of the ART module is also being used in
COSMO-ART. However, parameterisations differ and implementations have been
updated to newer Fortran standards. ICON-ART extends the numerical weather
and climate prediction system ICON with (gas-phase) chemistry, aerosol
dynamics and related feedback processes. A comprehensive treatment of
feedback processes between chemistry, aerosols, clouds and radiation can now
be included in recent and future studies
(Eckstein et al., 2017; Gasch et al., 2017; Rieger et al., 2017; Weimer et al., 2017).

Here, we will discuss the unified tracer framework of ICON-ART that allows to
define and specify tracer initialisation and a coupling of individual
chemical mechanisms and specific process modules. Depending on the
requirements of the application field, like large eddy simulations, numerical
weather predictions or climate simulations, ICON-ART can run with the
different existing physics configurations.

Given the multitude of challenges we are facing, models have to be capable of
being readily changed. Therefore, it is of high importance to provide a
tracer framework that is flexible and suitable for a large variety of
different applications. For the development of next-generation modelling
systems, the requirements for modern high-performance computer architectures
also have to be considered. Nowadays, numerical models are integrated on
massively parallel architectures with up to 106 cores. Unstructured
grids like the icosahedral ICON grid show advantages regarding the
performance on current high-performance computing systems
(Zängl et al., 2015). Taking into account the increase of computational
power over the last years, it is obvious that more diagnostic and prognostic
tracers will be included in future simulations than before. With ICON-ART
2.1, we introduce a new flexible tracer framework, which meets the demands of a
next-generation modelling system. There is full flexibility in defining the
tracers and associated characteristics. The set of tracers can be tailored
for individual experimental setups of different complexity. The model allows
for changes in the set of tracers without any recompilation. The ability to
replace the common usage of namelist structures, previously used by ICON-ART
and other models (Baklanov et al., 2014; Morgenstern et al., 2017), is fully
supported.

Here, we describe the concept of the new ICON-ART tracer framework. The
technical description is followed by examples of definitions for chemical and
passive tracers in ICON-ART in Sect. 5. In
Sect. 5, we give an overview of different applications of
ICON-ART using the flexible tracer framework. These applications include
simulations with the numerical weather prediction physics configuration and
simulations with the climate physics configuration. We finish with some
concluding remarks and an outlook.

For the simulation of the chemical composition of the Earth's atmosphere, it
is highly desirable to be able to design individual experiments on different
temporal and spatial scales. For example, if an experiment is focusing on
tropospheric chemical composition, it is useful to define many
emission-driven tropospheric chemical tracers. For long-term simulations, it might be
advantageous to define a smaller subset of chemical tracers leading to a less
expensive calculation in terms of computational time. In addition, sometimes
chemical tracers with a short lifetime, compared to dynamical timescales,
can be neglected in the transport processes.

With ICON-ART, we introduce a flexible tracer framework with a minimum of
predefinitions that allows all of the above and more. The user has the
ability to define a unique set of tracers, tailored to the requirements of
the model experiment planned. The new tracer framework builds on structures
which are already implemented in the basic ICON framework and shared by
different available physical configurations. These configurations are the LES
physics configuration (Dipankar et al., 2015; Heinze et al., 2017), the NWP physics
configuration and the climate physics configuration based on ECHAM6
parameterisation (Giorgetta et al., 2018). The new tracer framework
allows flexible coupling to the selected physical configuration. Thus, the
composition coupling can be performed without subsequent changes. The ART
code is not changed when the physics configuration is changed.

This allows an independent investigation of atmospheric processes using
different physical configurations.

2.1 Technical description of the tracer framework

The technical framework of ICON-ART is based on Fortran 2008, which
provides essential functionality for the new tracer framework. Classes allow
to overcome the stringent matching of data types. Items only have to match a
data type or any extension of this type when it is declared with
CLASS(type) where type is a derived data type
(Chapman, 2008). If the CLASS matches more than one data
type, it is called polymorphic. Within ICON-ART, we are using these
polymorphic objects to extend the existing tracer structure by additional
metadata. Linked lists are another Fortran 2008 feature that is
extensively used. Linked lists allow for new features like passing a
reference to the next list element or adding and deleting elements.

In the scope of this paper, we are working with two different types of
tracers: passive and chemical active ones. An example for a passive tracer is
a constituent that does not have any impact on other tracers or the
thermodynamics of the simulated system. Passive tracers have predefined
sources or only fixed initial conditions. They change by transport and do not
interact with other tracers or processes. Chemical active tracers experience
sources and sinks while being transported and can participate in feedback
processes.

Passive and chemical active tracers are distinguished by their associated
metadata. In our example, passive tracers have no chemical loss term to be
considered. In other words, the lifetime of passive tracers is infinite.
Information about the lifetime is used for simple chemical tracers that
experience an idealised loss while being transported. Of course, the
information dealing with transport should be provided for both tracer types.
A flexible aerosol dynamics module making use of the flexible tracer
framework is currently undergoing development.

2.2 Storage of metadata information

We extend the existing ICON-ART functionality by the usage of a key-value
storage. The storage is based on string comparisons using generic hash
tables. For every tracer, a unique key of fixed size is defined. This
represents the search key. The table is constructed like a dictionary. Tracer
information can be looked up fast, using this search key. Hash tables provide
the foundation for a generic and flexible concept to read and store metadata.
At model initialisation, one key-value storage for each tracer is initialised
by

CALL storage%init(lcase_sensitivity=.FALSE.)

In this case, the dictionary entries are not case sensitive. The storage unit
has two fundamental subroutines; these are storage%put and
storage%get. The first creates a new dictionary entry, and the
second searches for a key and gives the connected entry. In the next step,
this storage unit has to be filled. Accepted types of storage values are
reals, integers or characters.

2.2.1 Reading in metadata information from XML files

At one point in time, all numerical models need to find an answer to the
question how to transfer text-based information (e.g. tracer metadata) into
the model's program code. The text-based format XML (eXtensible Markup
Language) (W3C-Recommendation, 2017) gives the developer and the user
the ability to store and transport information in a structured way.

XML has only few mandatory rules; e.g. there has to be exactly one
root element. The framework in which the information is structured can be
freely chosen. The structure itself allows for a human readable format.
ICON-ART uses the Fortran interface TiXI
(https://github.com/DLR-SC/tixi, last access: 27 September 2018) to read the XML file. The TiXI
interface includes a flexible mechanism for XML file reading. Since the
scripting language of XPath is used, the navigation through an XML document
is an easy task to perform. The XML reading routine can be structured in the
same way as someone would read a document and remember the content in
the most natural way. An example structure of such an XML input file for the
tracer structure is the following:

The XML file is scanned automatically. For the realisation of this feature,
it is necessary to predefine the type of input. Every tag has a mandatory
type definition, i.e. char, int and real. The first word
in brackets, <tracers>, is called XML tag. The tag <chemical id=''TRO3''> has the additional attribute id for the tag
chemical. To identify a specific tracer, the system uses the given
attribute (e.g. id). Tags are used to build up the metadata
structure. It is a key-value storage where the tag (e.g.
mol_weight) is the key with the value, e.g. 4.800E-2 for
ozone. The number 4.800E-2 is then stored in the metadata structure
of ICON-ART. At this point, it should be noted that there are two kinds of
metadata: necessary and optional metadata. Necessary metadata depend on the
polymorphic type. Passive tracers do have different necessary metadata than
chemical tracers. The optional metadata are read in automatically. Every tag
in the XML file is translated into an entry in the key-value storage.

The structure of the ART tracer framework is shared with the core ICON tracer
framework and only expanded in the cases needed. In addition to that, tracers can
share attributes and are distinguished by additional attributes. In our
example, ozone appears in two different subroutines for chemistry: the first
using a lifetime-based mechanism, and the second using a full gas-phase
approach.

The tag <mol_weight> can be used for unit conversion within a
subroutine and is given in kg mol−1. The tag <lifetime>
is used for simplified integration methods for a given lifetime of a
species, in seconds. Some chemical substances or passive tracers do not have
to be transported; thus, the tag <transport> ensures that this
information is transferred to the program code and the transport is switched
on or off, respectively. In addition to that, templates can be defined. In
this case, a template named stdchem is chosen. At model startup,
this template is translated into a specific selection of horizontal and
vertical transport schemes. Each scheme stands for a specific numerical
discretisation of the mass continuity equation in horizontal or vertical
directions. Currently, there are three different default transport templates
available: off, stdaero and stdchem. These
templates avoid the necessity of adding a tracer advection scheme and flux
limiter for each single tracer in the namelist. Hence, the values of the
ICON-ART namelist parameters ihadv_tracer, ivadv_tracer,
itype_hlimit and itype_vlimit are overwritten by the
template. Specific information concerning the advection schemes mentioned can
be found in the official ICON documentation. Specifying off will
deactivate advective transport for this tracer completely.

The transport template stdchem uses the same advection schemes as
stdaero. However, the considerably faster positive definite flux
limiters are used instead of the monotone flux limiter. The conservation of
linear correlations is traded for a faster computation of the advection.

If chemical species need to be initialised, this is achieved via
<init_mode> and a respective number corresponding to the
initialisation scheme. If the init_mode is set to 0, no external
initialisation data are used. With integers different from zero, data from
other models, like EMAC (Jöckel et al., 2005) or MOZART
(Emmons et al., 2010), interpolated on the horizontal icosahedral grid are
read in and used. The vertical interpolation is performed online. Thus, the
initialisation dataset stays unchanged, regardless of the choice of model
levels for the simulation.

The structure of the key-value storage for every tracer is built up
automatically. This feature allows for an extensive flexibility which has to
be controlled. In our case, attributes are used in a fixed manner; they
define our basic framework of the tracer structure. The full flexibility,
direct access, control mechanisms and data type distinction are only a few
advantages of this XML-based procedure against other currently used
strategies.

2.3 Construction of the tracer metadata structure

Figure 1Schematic describing the tracer framework and the tracer definition
in ICON-ART.

Within ICON-ART, the metadata, e.g. regarding transport or chemical
properties, are read in using the XML interface. Afterwards, the information
is stored in the ICON-ART tracer framework. The individual steps of the
tracer transport simulation in ICON and ICON-ART are described in
Rieger et al. (2015). Figure 1 shows the calling
structure of the tracer framework in ICON-ART with regard to the tracer and
metadata construction. All tracers within the ICON-ART tracer framework share
a common structure like the following:

TYPE t_tracer_meta
...
END TYPE t_tracer_meta

In the case of chemical tracers, we want to provide additional information on,
e.g. mol weight. The extension of the derived type looks like this:

In this case, the mol_weight is mandatory information. Optional
metadata are stored in as part of the basic structure
t_tracer_meta. This container includes a key-value storage. This
solution ensures that the metadata container stays as flexible as possible.
The respective XML file is read in, and all information is processed and
stored in the opt_meta container; see Fig. 1. In
the example of mol_weight, this attribute becomes a real variable
accessible in the code. It can be accessed by using, e.g.

Here, CALL meta%opt_meta%get(…) is the operation to access
the key-value storage in the container.

The combination of linked lists, polymorphic classes and key-value storage
allows the user to define tracers in a flexible way. Only those tracers given
in an input file are read in, registered and appear in the modelling system.
With this solution, the user is free to define any number of chemical or
passive tracers which is only limited by the memory of the computing system.

2.4 Construction of XML tracer input file via MECCA

The XML file can either be edited manually by the user or generated
automatically by an external program. The tracer files used for the
experiments described in Sect. 5 are shown in
Appendix A. In this section, we demonstrate the XML
file generation by an external program, which also extends the functionality
of ICON-ART. In addition to the existing lifetime-based chemistry approach
(Rieger et al., 2015), a full gas-phase chemistry approach is added.

Construction of the full gas-phase chemistry approach is done using the
comprehensive and flexible atmospheric chemistry module MECCA (Sander et al., 2011).
MECCA provides a set of chemical reactions covering the troposphere as well
as the stratosphere. The base version of MECCA is extendable by its own chemical
reactions or an update of rate coefficients.

The MECCA preprocessing part has been extended by a routine constructing the
XML file for the tracer module of ICON-ART. The numerical flexibility of
MECCA is based on the KPP software (Sandu and Sander, 2006). KPP generates Fortran90 code which is used to solve the
differential equations based on the given chemical reaction. This is the
first step which is needed for KPP and thus for MECCA. In a second step, the
numerical integrator is chosen. For our example, we use the Rosenbrock solver
of the third order (Sandu et al., 1997). In the third step, we use the driver
part of MECCA. The driver stands for the main program which calls the
integrator, reads input datasets and writes the results in the original
MECCA model. Within ICON-ART, we replaced this step by routines of ICON and
ICON-ART. Only the integrator call element is maintained. A shell script, in
combination with the AWK scripting language (Aho et al., 1987) processes
all information and generates the respective files and routines. Technical
work has been done to ensure that a dictionary is accessible for the
translation between four-dimensional chemical tracers in ICON-ART and
one-dimensional concentrations of chemical species in the KPP routines.

Additionally, other chemical reaction mechanisms provided by MECCA can be
used within ICON-ART without changes in the program code itself. It is
sufficient to read the new tracer XML file generated by the MECCA
preprocessor. Finally, all standard reaction schemes provided by MECCA are
accessible in ICON-ART by only using MECCA as an external preprocessor. The
model has to be recompiled once, but the user does not have to perform
changes in the program code. Files that are changed by the preprocessor are
copied to the respective ICON directory automatically. Photolysis rates are
calculated using an updated version of CloudJ7.3 (Prather, 2015).

In the scope of this paper, we use a chemical reaction mechanism based on the
extended Chapman cycle to demonstrate the functionality of the ICON-ART
gas-phase routine.

Table 1Summary of the chemical reactions used for the extended Chapman
cycle simulation.

The general coupling structure using NWP physics configuration is described
in Rieger et al. (2015). The entry points of the coupling to the climate
physics configuration are different from the entry points for NWP. The general
concept of clear code distinction using compiler directives, described in
Rieger et al. (2015), remains the same.

Figure 2 shows a schematic overview of the calling
strategy of subroutines with the ECHAM (climate) physics configuration. Boxes
marked partly orange are shared structures between ICON and ICON-ART. Boxes
with an orange frame are processes affected by ART tracer tendencies. Boxes
with an orange background are ART routines. The model physics part is
represented using different blue boxes. The radiation subprocess is depicted
with a dark blue shaded box because the radiation process is called with a
reduced frequency than other processes, in general. Chemical reactions are
part of the physics routine in climate configuration but only active if ART
is compiled and chemical routines are activated by a namelist parameter. All
processes marked in a blue shade can have an individual calling frequency.

Most processes are treated in parallel-split mode which means that they are
all computed from the same state which is provided by the dynamics. The
tendencies returned by the individual processes are then summed up and
applied at the end of the physics time step. Condensation constitutes an
exception, as it is treated in sequential-update split mode. That is, it is also
computed from the latest post-dynamics state; however, the state is
immediately updated by adding the resulting tracer tendencies to the
respective state variables.

It is computationally expensive to couple global tropospheric and
stratospheric chemical models with global meteorological models. Therefore,
it is reasonable, especially at the beginning of the development of a model
like ICON-ART, to use simplified parameterisations for the description of
selected chemical species. Parameterisations of chemical species can save
computational costs and can give an initial overview of the general
applicability of the model. In addition to parameterisations of chemical
tracers, it is useful to define artificial passive tracers to investigate,
e.g. transport processes in the atmosphere. An example of a passive tracer is
the vortex tracer, described at the end of the following section. Further, we
describe a selection of parameterisations for the simulation of chemical
tracers in the atmosphere. These tracers can also be used to investigate
fundamental composition–circulation feedbacks. The selection is an extension
of the set of parameterisations described in Rieger et al. (2015) and
Weimer et al. (2017).

4.1 Linearised ozone algorithm

Ozone is one of the most important chemical species regarding radiative
heating in the middle atmosphere. Thus, ozone concentrations have to be
simulated reasonably to address questions about transport processes
determined by stratospheric processes
(Braesicke and Pyle, 2003, 2004). ICON-ART, in its standard NWP
configuration, uses monthly climatological ozone values derived from GEMS
climatology (Global and regional Earth-system (Atmosphere) Monitoring using
Satellite and in situ data, Hollingsworth et al., 2008). The ozone
climatology used in the heating rate calculations is independent from the
year of simulation. Here, we move one step forward and use the ansatz for a
simplified description based on McLinden et al. (2000). This ansatz can be
understood as a first-order Taylor expansion of the stratospheric chemical
rates. The ozone concentration tendency is linearised with respect to the
local ozone mixing ratio, temperature and overhead ozone column density. The
algorithm accounts for a more realistic vertical gradient than the provided
climatology. For the troposphere, we assume a constant lifetime of 30 days.

The following differential equation describes the linearised approach:

dξdt=P-L0+∂P-L∂ξ0ξ-ξ0+∂P-L∂T0T-T0(1)+∂P-L∂cO30c-cO30-1τpsc⋅ξ.

Here, ξ describes the ozone volume mixing ratio, T the temperature in
the respective grid box and cO3 the overhead ozone column.
The term (P−L) describes the ozone tendency, with P the production term
and L the respective loss term. Climatological values are indicated with
the superscript 0. The partial derivative with evaluation at the
respective climatological value is marked with a subscript 0. The
climatological values for P−L, cO3 and the respective
derivatives are given by a look-up table.

With this ansatz, heterogeneous processes are not taken into account. The
regular linearised ozone (Linoz) ansatz does not include the last term
(1τpsc⋅ξ). To address the catalytic
destruction by chlorine and bromine radicals in the presence of polar
stratospheric clouds, the linearised ansatz is expanded by an additional loss
term. Here, τpsc represents the lifetime of ozone in the
region where polar stratospheric clouds can potentially occur.

4.2 Vortex tracer

In the region of the polar stratospheric vortex, temperatures below
195 K can be observed. This low temperature regime determines the
development of polar stratospheric clouds. Chlorine and bromine activation
takes place on the surfaces of polar stratospheric cloud particles. This
activation leads to ozone destruction. In the past, it has been observed that
air parcels within the polar vortex seem to be isolated from midlatitudes
(Loewenstein et al., 1989; Russell et al., 1993), because the polar vortex edge can act as
a transport barrier.

To investigate processes which are relevant in the region of the
stratospheric polar vortex, we introduce a polar vortex tracer in ICON-ART.
The processes of interest include transport processes of air parcels from
within the vortex, exchange processes at the vortex edge and mixing
processes. The vortex tracer is a passive tracer with no destruction over
time. This means that the tracer is only affected by transport tendencies and
has no interaction with other tracers. To define the edge of the polar
vortex, the first approach is to use Ertel's potential vorticity (PV), given
in Km2kg-1s-1, which is defined as follows:

(3)PV=ζ+fρ∂θ∂z,

with ζ the relative vorticity in s−1, f the Coriolis
parameter in s−1, ρ the air density in kg m−3,
θ the potential temperature in K and z the geometric height
in metres. The relative vorticity is calculated by

(4)ζ=k^(∇×v),

with k^ the unit vector in the vertical and v the horizontal
wind field in m s−1.

On a timescale of weeks and on an isentropic surface, the PV is a conserved
quantity (Nash et al., 1996). The maximum in the PV gradient defines the
polar vortex edge. However, as this metric shows high variations in small
horizontal regions, distinct maxima cannot always be defined. Therefore, we
use a second metric following Nash et al. (1996). At the polar vortex
edge, the horizontal (zonal) wind component maximises and provides a
surrogate for the PV gradient. In the model, the following steps are
performed separately for each hemisphere: first, the PV is calculated at
every grid point. Between minima and maxima of PV, a sufficient number of
equidistant intervals is defined. Then, the respective geographical area
enclosed by PV isolines is calculated. The maximum westerly wind relative to
the area under PV isolines is derived. Multiplication of both values, the
maximum and the enclosed area, gives a reasonable constraint for the polar
vortex edge boundary region. The described definition holds for the location
of the Northern Hemisphere polar vortex. For the Southern Hemisphere, the
maximum of the easterly winds gives the constraint for the location of the
edge.

At the initialisation time step, the passive tracer is initialised to one
within the boundaries of the vortex. Afterwards only transport processes are
in action. This provides a useful metric to investigate transport processes
in the region of the polar vortex boundary.

4.3 Age of air

The characterisation of stratospheric transport from observations is
difficult, because the velocity of the meridional overturning (the so-called
Brewer–Dobson circulation) cannot be accessed directly. However, the
efficiency of the overturning can be estimated by investigating the trends of
volume mixing ratios of some long-lived trace gases
(Bönisch et al., 2011; Rosenlof, 1995). One of these species is
sulfur hexafluoride (SF6), which is an anthropogenically emitted
chemical compound with a long lifetime of up to thousands of years
(Ray et al., 2017). The increase of the middle atmosphere (up to
40 km) mixing ratio of SF6 can be assumed as quasi-linear
(Maiss and Levin, 1994). In addition, SF6 has no significant chemical
sink within the stratosphere. In the mesosphere, photolytic dissociation at
the Lyman-α band and dissociative electron detachment should be taken
into account (Ravishankara et al., 1993). With some assumptions, it is possible
to calculate the age of air by using the SF6 concentration at a
specific layer and horizontal location, and comparing (matching) it to past
tropical tropospheric values. Thus, the age of air represents the time that
an air parcel needed to travel from the tropical tropopause to this
particular location in the stratosphere. In the model, a simplified procedure
is followed: lower boundary conditions are updated every integration time
step to simulate a linear increase of SF6 with time. The increase
corresponds to an increase of the value of 1 year per year. Using the time
lag technique (Reddmann et al., 2001; Schmidt and Khedim, 1991), for pressure levels
above 950 hPa, one can calculate the age of air at this point by

(5)ψage=7⋅86400⋅365.2425+Δtsim,

with Δtsim is the integration time step of the model
given in seconds. After initialisation, the tracer ψage in
units of seconds is transported. To avoid small fluctuations, the mean age
of air is taken into account for further analysis. For the final calculation,
the simulated values are merged with the simulated time to get the actual age
of air (ψ′age) in seconds:

(6)ψ′age=ψage-tinit*-7⋅86400⋅365.2425,

where tsim* is the simulated time and tinit*
the time of initialisation, both given in seconds.

4.4 Water vapour

Water vapour has a strong impact on the radiation budget of the atmosphere in
the long-wave infrared spectrum. On the one hand, the infrared emission of
water vapour leads to a cooling (loss in the thermal budget) of the
atmosphere. Thus, the concentration of water vapour influences transport
processes due to thermodynamic induced changes in the wind field. On the
other hand, transport processes affect water vapour concentrations. The
investigation of water vapour in models and observation allows studying
global circulation patterns (Kley et al., 2000). The amount of water vapour
entering the stratosphere depends on the temperature within the tropical
tropopause layer (TTL). Above, the Brewer–Dobson circulation drives the
upward transport in the tropics. Below, convective fluxes and slow ascend in
the TTL determine vertical transport. The process of “freeze-drying” leads
to dry air parcels entering the stratosphere, because ice crystals sediment
out (Brasseur and Solomon, 2006). This process is included in the
microphysical schemes of the NWP and the climate physics. Methane oxidation
is a chemical source for stratospheric water vapour. Higher up, methane
oxidation is a chemical source for stratospheric water vapour. However, the
photodissociation of water, mainly located in the mesosphere, is an important
sink for water vapour in the atmosphere.

with τCH4 the lifetime of methane in seconds, p the
pressure and α=19ln⁡(10)ln⁡(20)4. Based on
Brasseur and Solomon (2006), the lifetime of water vapour
(τH2O) due to photodissociation can be calculated by

(8)τH2O=3p≤0.1Pa100+1+αln⁡p504ln⁡10000p0.1Pa<p<20Pa∞p≥20Pa.

Taking both terms of production and loss into account, the volume mixing
ratio of water vapour at time step t+Δtsim can be
calculated by

(9)ψH2O(t+Δt)=ψH2O(t)+Δt21τCH4ψCH4(t)-1τH2OψH2O(t),

with ψCH4 in mol mol−1 as the methane tracer of
ICON-ART.

Figure 3Antarctic total ozone column (DU) for a time sequence starting on 21
September 2002 finishing 1 October 2002 (left to right). Daily averages for
two ICON-ART simulations for the Linoz chemistry scheme (a–d) and
for the extended Chapman cycle chemistry (e–h). Total Ozone Mapping
Spectrometer (TOMS) observations are shown in panels (i–l). The
model was initialised on 20 September 2002.

Studies have shown that observed mean-annual cooling trends in the tropical
tropopause are larger than shown by model simulations, e.g.
Shine et al. (2003). It can be seen that the large-scale dynamics in
the Earth's atmosphere, tropical tropopause temperatures and lower
stratospheric water vapour are closely linked to each other by complex
feedback processes. By using the parameterisations described above in
ICON-ART, we want to account for these important feedback mechanisms in a
simple but reasonable way. To investigate the feedback processes of the ART
water vapour tracer (including radiation), only the tendencies from the methane
oxidation and photodissociation are taken into account. At every model time
step, the water vapour mass mixing ratio is set to the value of the
qv tracer, which is affected by the microphysics routines of ICON.
The qv tracer is the standard water vapour tracer of ICON.
Within the ICON-ART routine, the tendency due to methane oxidation and
photodissociation can be added. In the last step, qv is set to the
value of ψH2O. The standard tracer qv is
overwritten before other processes like radiation or microphysics are active.

Here, we use ICON-ART and its new tracer framework with the NWP and the
climate physics configuration in different applications. First, we discuss a
stratospheric hindcast experiment based on the ICON NWP configuration,
focusing on different chemistries (Sect. 5.1).
Second, we show some climatological applications based on the ICON climate
physics configuration, illustrating the impact of chemical composition
feedbacks.

5.1 Ozone and vortex tracer

In 2002, an unusual split of the ozone hole was observed on 22 September 2002
and described by Newman and Nash (2005). The initiation of the splitting
process is not fully understood yet. The split of the ozone hole had no
chemical reason; instead, it is a dynamical change controlling atmospheric
composition and in particular ozone distributions. Several studies, e.g.
Matsuno (1971), have shown that a vortex split event can be caused by
atmospheric interactions with upward propagating planetary waves.
Sinnhuber et al. (2003) pointed out that the major stratospheric warming
occurred far earlier than the normal final warming at the end of the ozone
hole season. With ICON-ART, we are able to study the vortex split event of
2002 in a hindcast. First, we want to discuss the total ozone column
simulated with ICON-ART. For the setup of the experiment, we use reanalysis
data from the European Centre for Medium-Range Weather Forecasts (ECMWF) –
ERA-Interim (Dee et al., 2011) to initialise the meteorological variables (e.g.
pressure, temperature, water vapour and horizontal wind fields). The hindcast
is initialised on 20 September 2002, 00:00 UTC. The chosen grid is R2B6,
corresponding to an approximate horizontal grid spacing of 40km(Zängl et al., 2015). The model top is at 75 km with 90 vertical
levels. The integration time step is 240s, and the output time
step is every hour. The simulated ozone column, interpolated on a regular
latitude–longitude grid with a resolution of 0.5∘, is shown in
Fig. 3. The four columns show daily means of total
ozone for the respective dates. The mean is taken over all 24 output steps
starting at 00:00 UTC. For the initial values of ozone, the ERA-Interim data
are used analogous to the meteorological data. The ozone hindcasts are
performed with two different schemes: the modified Linoz scheme as described
in Sect. 4.1 and a gas-phase algorithm (the extended Chapman
cycle) with reactions described in Table 2.4.
For the comparison between simulations and measurements, we are using
satellite observations from the Total Ozone Mapping Spectrometer (TOMS)
instrument (TOMS Science Team, 2016).

Figure 4Passive and active ozone tracers (mol mol−1) at 50 hPa
over Antarctica. Daily averages for two ICON-ART simulations for the Linoz
chemistry scheme (a) and for the extended Chapman cycle
chemistry (b) and the passive tracer (c) are shown.

Figure 5Difference of the passive and active ozone tracers (mol mol−1)
at 50 hPa over Antarctica. Daily averages are shown for two ICON-ART
simulations for the Linoz chemistry scheme (a) and for the extended
Chapman cycle chemistry (b). For the loss rate of the Chapman cycle,
the zero contour line is added. Shaded areas indicate temperature regions
below 195 K.

A total of 5 days after initialisation, the represented horizontal geometry of the
total ozone column in both ICON-ART simulations was slightly different in
comparison to the satellite observations. After more than 10 days of
simulation, the shape of the vortex was still in good agreement with satellite
observations. Within the polar vortex, the total ozone column reached values
of about 200 DU in both, ICON-ART simulations and the satellite
observations. At the end of the simulation, on 1 October 2002, the largest
difference between the extended Chapman cycle simulation, using the full
gas-phase algorithm, and the simplified Linoz simulation was found at
70∘ S, 120∘ W, outside of the polar vortex. The total ozone
column reached values of 450 DU and above for the extended Chapman
cycle simulation. For the Linoz simulation, ozone destruction was slightly
higher and values up to 400 DU were reached. The comparison to the
TOMS observation shows that this minimum fits to observation. However, the
horizontal pattern slightly differs. The observation also shows small areas
with up to 420 DU.

In order to illustrate the differences between the two idealised simulations
(parameterised using Linoz; gas-phase chemistry only with an extended Chapman
cycle), the total contribution of chemical tendencies is depicted in
Figs. 4 and 5. We are
using the differences between a passive (no chemical changes after
initialisation) ozone tracer and the chemical ozone tracers for Linoz and
extended Chapman cycle chemistry. The difference represents the chemical
ozone loss.

The passive and the chemical ozone tracers are initialised identically.
Technically, this is ensured by declaring the ozone tracers to be the same
type as the chemical tracer but without chemical interactions. The
corresponding XML entry can be found in Appendix A1.
In Fig. 5, at 70∘ S, 120∘ W, on
1 October 2010, ozone loss is dominated by chemical loss. Ozone losses are
higher in that region. The passive tracer, which is only affected by
dynamical tendencies, has higher values than the chemical tracers; thus, ozone
has been depleted by the chemical mechanisms. The maximum difference between
passive and chemical tracers is up to 10 times higher for the Linoz
simulation than for the extended Chapman cycle. For visualisation purposes,
the contour line representing no loss (0 mol mol−1) is added.
This is to be expected, because the initial ozone distribution is not
equilibrated with respect to the Linoz reference state (causing large
tendencies) and we do not consider additional loss terms by heterogeneous
processes or halogens in the extended Chapman cycle chemistry (limiting the
range of chemical tendencies). However, we can focus on the general spatial
structures and how transport has modified ozone distributions. Inside the
polar vortex, on 1 October 2002, we model ozone production for both
simulations. The chemical tracer in both simulations is increased with
respect to the passive one. The increase is higher in the Linoz simulation
than in the extended Chapman cycle. This implies that temperatures in that
region are not low enough to trigger the heterogeneous destruction of ozone
in the Linoz scheme. Outside the polar vortex, mainly on 25 September, high
values of ozone loss can be observed for the Linoz simulation but not for the
extended Chapman cycle. This is also caused by the difference in addressing
heterogeneous destruction. Within the Linoz scheme, the loss term has been
triggered and we can observe additional ozone loss. This feature is missing
for the extended Chapman cycle chemistry.

In Fig. 6, the temporal evolution of the passive vortex
tracer is depicted. The colour coding gives the values of the vortex tracer
at an interpolated pressure level of 30hPa at midnight on the
given date. Again, the date of initialisation is chosen to be
20 September 2002 and within the boundaries of the vortex; the tracer is set
to a value of 1. Here, only transport takes place. The horizontal spreading
of the vortex tracer depicts the dynamical evolution of the vortex in the
Southern Hemisphere. The horizontal spreading and steep tracer gradients
correspond to the horizontal distribution of the total ozone column in
Fig. 3. At the day of initialisation, the vortex is
still intact, but in the following days the first observed major
stratospheric warming in the Southern Hemisphere, e.g. (Newman and Nash, 2005),
occurs. The massive outflow of vortex air masses, beginning on
24 September 2002, can be visualised by the vortex tracer distributions. This
outflow is correlated to the increased dynamical impact on the vortex
integrity. The vortex split occurred on 26 September 2002. The structure,
represented by the spatial distribution of the vortex tracer, is nearly
separated. On this day, the northernmost latitude of 30∘ S is
reached by a vortex filament. The vortex tracer allows to define regions of
isolated air masses within the vortex, thus providing an insight into the
chemical composition changes that are least affected by diffusion and mixing
(e.g. McKenna et al., 2002 or Konopka et al., 2005).

Figure 6Daily snapshots at 00:00 UTC at 30 hPa of the passive
vortex tracer in arbitrary units (upper left to bottom right). The passive
tracer was set to 1 within the vortex boundary at the beginning of the
integration.

5.2 Feedback of ozone on radiation

In the previous section, we have shown that the Linoz configuration of
ICON-ART provides good hindcasts on days to weeks. The limitation that the
initial state should be equilibrated, mentioned above, does not matter after
a spin-up period. Here, we will extend the time horizon considered to decadal
integrations. In addition, we will illustrate how the optional composition of
tracers with radiation feedback affects the system. With the flexible tracer
structure, the user has the ability to switch on the radiative feedback by
the tag

<feedback> 1 </feedback>.

Thus, no changes in the code have to be made by the user. Only the XML file has to be changed.

The ICON-ART simulation is configured as an AMIP (Atmospheric Model
Intercomparison Project) like experiment (Gates et al., 1999). The boundary
conditions used are summarised in Table 2. We set up two
experiments on the R2B4 grid which corresponds to an effective horizontal
grid spacing of 160 km. The integration time step is chosen to be
600 s. Output is written every 3 days and interpolated onto
predefined pressure levels, corresponding to the standard ERA-Interim
pressure levels. The first experiment uses an ozone climatology with monthly
mean values based on Cionni et al. (2011). This simulation is called
“control”. The feedback simulation uses the interactive ozone as well as the
additional tendencies on water vapour, shown in Sect. 4.4.
For all following diagnostics and discussions, the same two simulations are
used. The results of the complete integration from 1979 to 2009 are found in
Appendix B. In the following, analysis of the time span from
1990 to 2009 is shown. This avoids possible differences arising from changes
in ozone depleting substances from 1979 to 1990 that are not considered in
Linoz with time-invariant look-up tables.

Figure 7 shows the climatological ozone distribution at a
pressure level of 50 hPa. Monthly averaged zonal means of ozone for
the period of 1990 to 2009 are plotted twice. Figure 7a shows the ozone
used in the control run (ctrl) (Table 2). Figure 7b shows
the modelled ozone of the non-interactive Linoz simulation, where modelled
temperatures are the same as in the control run. Figure 7c shows the
temporal evolution of the zonal mean ozone in the feedback simulation.
Additionally, contour lines are representing the interannual standard
deviations of ozone in all panels. The black contour line represents a
standard deviation of 1×10-7kg kg−1. Brighter colours
present higher values of standard deviation with a spacing of
2×10-7kg kg−1.

A striking difference in the three ozone distributions shown in
Fig. 7 is the duration of the ozone hole period indicated by
the area that is shaded in dark blue. Modelled ozone (Fig. 7b, c)
shows a much longer duration than the assumed background climatology. In
addition, the duration of the ozone hole period increases slightly for the
interactive integration. This is caused by the feedback of the modelled
ozone, calculated with the Linoz parameterisation under consideration of the
correction term for a shorter ozone lifetime due to the presence of polar
stratospheric clouds. From October to December, very low ozone concentrations
of about 1×10-6kg kg−1 can be seen in the panel for the
non-interactive Linoz ozone (Fig. 7b). For the feedback simulation, low
ozone concentrations in the Southern Hemisphere, in conjunction with low
temperatures, occur until the end of spring. The feedback process
stabilises the Southern Hemisphere polar vortex, prolonging its lifetime by
delaying the final warming. The default climatology of the ICON-ART control
simulation does not represent very low values of ozone concentrations. This
misrepresentation has also been discussed by, e.g. Arblaster et al. (2014).
Here, the authors point out that most models that are using a prescribed
ozone climatology tend to underestimate the Antarctic ozone depletion. Thus,
modelled ozone values are higher than indicated by observations between 1979
and 2007 (Hassler et al., 2013). Taking the standard deviation into
account, the characteristics of the AMIP ozone climatology
(Cionni et al., 2011) become clearer. The contour lines represent a
semicircle each winter in the Southern Hemisphere that is aligned with the
ozone concentration gradients. This symmetric and coherent pattern of the
ozone minimum is most likely not a very realistic representation of
variability on top of an ozone hole that is not deep enough. The standard
deviation isolines for the modelled ozone are different and intersect the
isolines of ozone concentrations, with higher variabilities at later times in
the ozone hole period.

We note that the standard deviations shown in Fig. 7 should
be interpreted differently between the control and feedback simulations of
ICON-ART. This is due to the fact that in the control simulation a
time-varying background climatology is used that has an imposed long-term
trend, whereas in the feedback simulation, no trend is imposed, and the
internal variability of ICON-ART is the main component of the ozone
variability. To illustrate this difference, we summarise the results of a
time series decomposition in Table 3.

We use the time series of the 60∘ S zonal mean total ozone column of
both ICON-ART simulations. For comparison, we analyse TOMS observational data
(TOMS Science Team, 2016), also at 60∘ S. The chosen latitude allows a continuous
time series analysis. We separate the time series of the ICON-ART simulations
in two periods: 1980 to 1997 and 1997 to 2009. The ICON-ART control
simulation shows an ozone decline for the earlier period and a small ozone
increase for the later period (by construction of the background
climatology). The feedback simulation shows no pronounced trend (as expected;
see above) in particular during the later period. Note that the RMSE (root
mean square error) is higher in both periods for the feedback simulation
compared to the ICON-ART control simulation. The higher RMSE is induced by
substantial year-to-year meteorological variability. Both model time series can
be compared to the shorter TOMS on NIMBUS 7 time series (1997 to 2005).

Table 3Results of the time series analysis for different time domains at
60∘ S for the ozone total column. Results for both ICON-ART
simulations and TOMS observation are shown. The early period is from 1980 to
1997 and the late period is from 1997 to 2009 (to 2005 for TOMS on NIMBUS 7).
The the annual cycle (AC) minimum and maximum anomalies are derived from the
full time series.

5.3 Temperature changes due to ozone feedback

In the previous section, we have shown that with the change to an interactive
representation of ozone the duration of the southern polar vortex is
increased. Here, we provide more details on the zonal mean temperature
distributions in the control and feedback simulations.

The direct impact of ozone, already mentioned in
Sect. 5.2, is also displayed in the seasonal
variation of the zonal mean temperatures; see
Fig. 8.

In the Northern Hemisphere winter (DJF), temperatures of 210 K are
reached between 100 and 10 hPa in the tropics. For the season of
June–July–August (JJA), the temperature minimum in the tropics is at
100 hPa with temperatures as low as 200 K in both ICON-ART
simulations. In the Southern Hemisphere winter, ICON-ART control reaches
temperatures of about 200 K and ICON-ART feedback temperatures lower
than 195 K. The Northern Hemisphere summer is represented by
temperatures higher than 260 K above 5 hPa.

Figure 9 shows the
difference between control and feedback runs. In the Southern Hemisphere
winter, the effect described in Sect. 5.2 can be
observed. Due to the lower polar vortex temperature, differences up to
5 K occur. The control run shows warmer temperatures in the southern
polar region (below 20 hPa). Above this altitude, the feedback run is
warmer than the control run. This is due to the different ozone distributions
in the Southern Hemisphere winter. Within the tropical stratosphere,
temperature differences of about 2.8 K can be seen. The differences
of zonal and seasonal mean temperatures between ERA-Interim and the ICON-ART
feedback simulation are shown in
Fig. 10. The feature of a
long-lasting Southern Hemisphere polar vortex is present as well, seen in high
temperature differences of up to 20 K from September to November
(SON). In general, the ICON-ART control simulation shows warmer temperatures
than the ICON-ART feedback simulation, except for high altitude ranges above
5 hPa.

The general structure is comparable to the results shown in the comparison
studies of ECHAM5 (Roeckner et al., 2006). The difference between
ERA-Interim and ICON-ART is increased in the Southern Hemisphere
stratosphere. Nevertheless, the representation of the polar vortex seems to
be more realistic in the ICON-ART feedback simulation than in the control
simulation. In the vertical region around 50 hPa, the difference
between ERA-Interim and the feedback simulation is about 15 to 20 K
in the Southern Hemisphere and below 8 K in the tropics.

5.4 Zonal wind fields

Changes in temperature due to radiative feedback effects of ozone are also
affecting the zonal wind structure. The zonal mean zonal wind is shown in
Fig. 11. The left column shows the seasonal mean of the
control ICON-ART simulation and the right column the feedback simulation. In
both simulations, a strong eastward zonal wind with wind speeds up to
60 m s−1 is reached in the Southern Hemisphere winter (JJA). The
wind speed patterns in the tropical stratosphere also match the seasonal
mean analysis.

Figure 12 shows the differences between the
ERA-Interim and the results of the ICON-ART feedback simulation. Strong
differences in the stratospheric zonal wind can be seen in the Northern
Hemisphere winter. Here, ERA-Interim shows values up to 25 m s−1
lower than in the ICON-ART simulation. Between 30 and 60∘ N
latitude above 20 hPa, the sign of the differences changes. Here, we
observe stronger zonal wind speeds than in ERA-Interim. The overall patterns
are similar to the differences shown in Roeckner et al. (2006).

5.5 Water vapour

Here, we focus on the atmospheric water vapour tape recorder in ICON-ART. An
atmospheric tape recorder can be defined as the vertical propagation of an
anomaly that varies periodically in time with a tropospheric source
(Gregory and West, 2002). The temporal and vertical distribution of the
tropical stratospheric water vapour is a prominent example for an atmospheric
tape recorder signal. The simulated stratospheric water vapour depends
strongly on the temperatures that are encountered by an air parcel containing
water vapour that is transported vertically from the troposphere upwards
towards and through the tropopause (Schoeberl et al., 2012). The quantitative
link between variations of tropical tropopause temperatures over decades and
their influence on water vapour transfer into the stratosphere is still not
fully understood (Rosenlof and Reid, 2008). It has been shown that
stratospheric water vapour can have a strong impact on stratospheric climate
(de F. Forster and Shine, 1999). Thus, the study of the water vapour tape
recorder is an important tool for the further understanding of large-scale
transport processes and climate change linkages.

The tape recorder anomalies are calculated relative to the annual mean water
vapour in the tropics (5∘ N–5∘ S). For this study, we use
the water vapour tracer, qv. This tracer is the standard tracer of
ICON itself. It is used in the radiation scheme and is not only transported
but is also affected by the microphysical schemes. As in the previous
section, ozone is calculated with the Linoz scheme and used interactively.
The additional water tendencies by methane oxidation and photolysis is also
included in the stratospheric water budget.

Figure 13Tropical (5∘ N–5∘ S) water vapour anomalies as
monthly mean deviations (plotted twice) from the annual mean, averaged from
1990 to 2009, as a function of months and altitude.

Figure 13 shows the stratospheric tape recorder. For the
analysis, the years 1990 to 2009 are taken into account. We compare the
ICON-ART results with the water vapour product from ERA-Interim. The
calculated mean ERA-Interim tape recorder is shown in the bottom panel of
Fig. 13.

The tape recorder signal for the ICON-ART control simulation shows lower
values of anomalies down to -7×10-7kg kg−1 in
comparison to the feedback simulation (Fig. 13). For
ERA-Interim, anomalies up to -2×10-7kg kg−1 are
diagnosed from February to June in the pressure range of 100 to
50hPa.

The anomalies in the feedback simulation show higher absolute values. The dry
anomalies in the control simulation, between April and June, are decreased in
the feedback simulation. In addition, the positive (wet) anomalies from June
to December, between 60 to 20 hPa, are decreased in the feedback
simulation. The water vapour tape recorder shows less pronounced dry
anomalies in the tropical stratosphere in the feedback simulation due to the
additional source of water vapour by methane oxidation. Additionally, the
analysis of the water vapour tape recorder is consistent with the results of
the temperature differences, as seen in
Fig. 9. Due to lower
tropical tropopause temperatures in the feedback simulation, less water can
enter the lower stratosphere. The annual mean temperature difference (not
shown) shows that the control simulation is up to 2.8 K warmer than
the feedback simulation between 100 and 50 hPa, in the tropics. The
results correspond with the freeze-drying hypothesis explained above. In
general, monthly mean anomalies are attenuated in the feedback simulation
compared to the simulation using the standard ozone climatology, with in
particular the winter months being more similar to ERA-Interim. Focusing on
the strong positive anomaly between 100 and 50 hPa, from May to
August, the explanation from above has to be extended. The annual mean shows
a cold bias of the tropical tropopause. However, referring to
Fig. 9, one can see
that, for that time span, the sign of temperature changes. In the feedback
simulation, higher temperatures in the tropical tropopause are reached. Thus,
more water vapour can enter the stratosphere which is a possible explanation
for the strong positive anomaly.

The tape recorder signal of ERA-Interim shows a maximum amplitude in the
lower stratosphere between 100 and 60 hPa. In the ICON-ART control
simulation, the lower stratospheric amplitude of the water vapour tape
recorder is already smaller. With interactive ozone and water vapour, as in
the ICON-ART feedback simulation, the lower stratospheric amplitude is
attenuated further. However, a relative maximum above 20 hPa occurs.
This is likely the result of an overestimated water vapour tendency provided
by the methane oxidation, because methane is biased slightly high in the
feedback simulation.

The slope of the latitude–height water vapour anomalies is nearly unchanged
between non-interactive and interactive integrations. Thus, the velocity of
the upward transport is largely unaffected by the inclusion of the radiative
feedback. The result of both ICON-ART simulations in comparison to
ERA-Interim is similar to the results presented in Jiang et al. (2015). Here,
the authors combined measurements and simulations of water vapour from the
Microwave Limb Sounder (MLS), GMAO Modern-Era Retrospective Analysis for
research and Applications in its newest version (MERRA-2) and ERA-Interim and
used them for a comparison of the water vapour tape recorder behaviour. The
upward transport above the tropical tropopause of ERA-Interim is found to be
faster than the transport diagnosed from MLS measurements. Thus, in the
current configuration, ICON-ART produces similar ascent rates to ERA-Interim,
which are likely faster than observed.

We have shown that the change between interactive and non-interactive
integrations with respect to tropical ascent rates is small. However, some
changes are clearly detectable and the important relationship between the
tropical tropopause minimum temperatures and water vapour concentrations is
qualitatively captured by the ICON-ART system. More comprehensive climate
studies are in preparation.

5.6 Age of air

For the simulation of the age of air, we use the same setup as described in
Sect. 5.3. As all other diagnostics, the age of
air tracer is interpolated on a regular latitude–longitude grid with a
horizontal resolution of 0.75∘×0.75∘ on predefined
pressure levels.

Figure 14Latitude–height cross sections of seasonal and zonal mean ages of air
(year) for ICON-ART simulations from 1990 to 2009. (a) Control run;
(b) feedback simulation.

Figure 15Monthly averaged zonal means of age of air (year) at 50 hPa
(shown twice) for the period from 1990 to 2009 (shaded). Contour lines
represent the standard deviation of the monthly means. (a) Control
run; (b) feedback simulation.

The tracer is initialised as described in Sect. 4.3. The
control (ctrl) experiment is a simulation in which water vapour and ozone
calculated within ICON-ART have no impact on the calculation of radiation. In
the second experiment (feedback), the altered ozone and water vapour
distributions of ICON-ART are coupled to the radiation routine. The first
11 years of each simulation are excluded in the analysis to prevent
spin-up effects contaminating the result.

The ICON-ART modelled age of air is depicted in
Fig. 14. The diagnostic of age of air can be seen
in this case study as an important tool to analyse the feedback processes of
greenhouse gases on transport processes in the Earth's atmosphere. It can be
seen that the mean age of air is younger; thus, the upward transport has been
faster in the control simulation. Due to upwelling transport processes in the
tropics, the youngest air masses can be found there. The polar regions show
the occurrence of older air masses up to an age of 4 years in both
simulations. The asymmetry between Southern Hemisphere and Northern Hemisphere, induced
by faster circulations in the Southern Hemisphere
(Mahieu et al., 2014), is also well captured. One can clearly see the
impact of ozone and water vapour on radiation and thus on transport
processes. The age of air in the feedback simulation is up to 6 months
older than the control simulation. Figure 15
shows the climatological mean age of air, in the same representation as shown
in Fig. 7. Here, the temporal and zonal means of the age of
air from 1990 to 2009 are taken at an altitude of 50 hPa. The standard
deviation from the mean is represented by the contour lines. These lines
represent the interannual variability. The absolute mean age of air is
higher for the feedback simulation on both hemispheres. The band of low
values in the tropics is narrowed for the feedback simulation. The values of
standard deviation are comparable. However, in the region of the Southern
Hemisphere polar vortex, from October to January, the standard deviation is
higher for the feedback than for the control simulation. Since the polar
vortex is stabilised by the ozone feedback, a different dynamical situation
can be observed, influencing the interannual variability of the age of air.
In comparison to other studies, which focus on other time spans
(Brasseur and Solomon, 2006; Engel et al., 2009; Haenel et al., 2015; Stiller et al., 2012), ICON-ART shows an age of air which is too young compared
to observations. But this behaviour has also been observed in other studies
with different models, as described in, e.g. Monge-Sanz et al. (2007) or
Hoppe et al. (2014). With this diagnostic, the general representation of
stratospheric transport processes can be investigated further.

We present a new flexible tracer framework developed for ICON-ART. The
next-generation model ICON-ART can be used for many different applications
currently ranging from forecasting to climate simulations. ICON is used for
LES simulations, operationally for numerical weather forecasting and for
climate simulations. All three application areas have very different demands
in terms of model configurations, including the set of tracers and tracer
interactions to be simulated. For future studies using ICON-ART, a fast
adaption of the selected tracers and interactions to the experimental
requirements is of high importance. With our new flexible tracer framework,
tracers can be added and configured without any changes to the model source
code. This allows users to easily perform complex model experiments. In a
forward-thinking manner, we also provide the option to extend the existing
tracer structure and submodule awareness of tracer subsets. Within the scope
of the paper, we demonstrate the tracer framework and its applicability for a
range of simulations. We present one hindcast case study and AMIP-type
climate integrations.

In the first instance, we included a parameterised chemistry (Linoz) and a
gas-phase chemistry (extended Chapman cycle) into the NWP configuration of
ICON-ART. With this setup, we perform a successful hindcast experiment of the
ozone hole split in the year 2002 and characterise the (chemical) ozone
changes during the hindcast period. Using a diagnostic vortex tracer, we can
identify the vortex remnants that are least influenced by chemistry. Results
are consistent with expectations and previous work and illustrate the ability
of the ICON model to produce a good stratosphere forecast on timescales of
days to weeks. In this situation, dynamics is driving the ozone distribution
and the spatial patterns correspond well to observations. However, ozone
amounts differ between the two different chemical mechanisms. The setup can
be easily extended by additional chemical reactions and diagnostic tracers.

In the second instance, we include a parameterised chemistry (Linoz), a
methane oxidation scheme and a diagnostic tracer (age of air) into a climate
configuration of ICON-ART and perform AMIP-type integrations. We perform
decadal non-interactive and interactive integrations and compare the
performance of both simulations to each other and to ERA-Interim. For the
interactive integration, we couple ozone and water vapour to the radiation. In
the interactive simulation, the ozone hole season is extended, the tropical
upwelling is only weakly affected, and the overturning circulation as measured
by the Brewer–Dobson circulation shows a Northern Hemisphere age increase up
to 1.5 years. The base climatology of ICON is not affected by ART in the
non-interactive simulation. In the interactive simulation, some changes to the
climatology of temperature and winds occur; none of the changes are
detrimental to the model, and some are even beneficial.

For all experiments, no changes in the ART source code were necessary to
change from NWP simulations to climate integration. Only the XML file differs
between the full gas-phase (extended Chapman cycle) simulation and the
parameterised (Linoz) one. This paper demonstrates the flexibility of the new
tracer framework for ICON-ART, which suits the demands of a large variety of
different applications ranging from NWP to climate integrations.

Currently, the legal departments of the Max Planck Institute
for Meteorology (MPI-M) and the DWD are finalising the ICON licence. If you
want to obtain ICON-ART, you will first need to sign an institutional ICON
licence, which you will get by sending a request to icon@dwd.de. In a second
step, you will get the ART licence by contacting Bernhard Vogel
(bernhard.vogel@kit.edu). Versions are controlled by GIT repositories, and a
tar ball of the latest official release is provided to the licensee.

Schmidt, U. and Khedim, A.: In situ measurements of carbon dioxide in the
winter Arctic vortex and at midlatitudes: An indicator of the “age” of
stratopheric air, Geophys. Res. Lett., 18, 763–766, https://doi.org/10.1029/91GL00022,
1991. a

In this paper, we introduce the most up-to-date version of the flexible tracer framework for the ICOsahedral Nonhydrostatic model with
Aerosols and Reactive Trace gases (ICON-ART).
We performed multiple simulations using different ICON physics configurations for weather and climate with ART.
The flexible tracer framework within ICON-ART 2.1 suits the demands of a large variety of different applications ranging from numerical weather prediction to climate integrations.

In this paper, we introduce the most up-to-date version of the flexible tracer framework for the...